Underfitting refers to the situation where the model does not fit the training data well. It is usually used to evaluate the learning and generalization ability of the model.

The definition of a classifier is to construct a classification model based on existing data. The model can map the data records in the database to one of the given categories, so as to be applied to data prediction. The construction and implementation of the classifier generally go through the following steps: Select samples (including positive samples and negative samples […]

Weight is a relative concept, referring to a certain indicator. The weight of an indicator refers to the relative importance of the indicator in the overall evaluation.

Undersampling is a method to alleviate class imbalance by discarding some samples, that is, appropriately undersampling the categories with a large number of samples (majority classes) in the training set.

Soft margin is a method used to deal with linear inseparable problems and reduce the impact of noise. Soft margin is a method that allows some errors to exist during classification.

The radial basis function (RBF) is a scalar function that is symmetric along the radial direction. It is usually defined as the distance from any point X in space to a certain center X._c It can be written as K ( || X – X _c || ), its effect is often local, that is, when X is far away from X_c The function value is very small.

Quantum computing is a new type of computing based on quantum effects. The basic principle is to use quantum bits as information encoding and storage units, and to complete computing tasks through the controlled evolution of a large number of quantum bits. Comparison between quantum computing and traditional computing (1) Information expression In traditional computing, the unit of computer operation is a ratio of 0 or 1 […]

A quantum computer is a device that uses quantum logic to perform general computations. It is a specific implementation of quantum computing.

Quantum neural network (QNN) is a network composed of several quantum neurons according to a certain topological structure.

Robustness refers to the ability of a computer system to handle errors during execution and the ability of an algorithm to continue to operate normally when encountering anomalies such as input and calculation.

Supervised learning is a machine learning method in which the output is related to the input. A pattern can be learned or established from the training data, and new instances can be inferred based on this pattern.

Structural risk is a compromise between empirical risk and expected risk. A regularization term (penalty term) is added after the empirical risk function to obtain structural risk.

Structural risk minimization (SRM) is an inductive principle in machine learning. It is often used as a strategy to prevent overfitting.

The squeeze function is a function that squeezes a larger range of input into a smaller range. It is often used as an activation function.

Weighted voting is a voting method that takes weights into account.

Neighbor Component Analysis (NCA) is a distance measurement learning method associated with KNN (K Nearest Neighbors), which belongs to supervised learning methods. It was first proposed by Goldberger et al. in 2004.

The intra-class scatter matrix represents the scatter of each sample point around the mean.

Comprehensibility refers to how easy something is to understand, mainly whether it is easy for readers to understand.

Polarity detection is the process of classifying the sentiment polarity of text in natural language.

The activation function is a dynamic principle that is often used in neural network models. It defines how a neuron changes its activation value based on the activity of other neurons. The general activation function depends on the weights in the network, which can introduce nonlinear factors and is often used to solve problems that cannot be solved with linear equations.

A parse tree, also called a concrete syntax tree, is a representation of the results of a syntax analysis, which represents the grammatical structure of a language in a tree shape.

Structure is a method of displaying a neural network topology diagram, which is often used in the field of neural networks. In a neural network, variables can be the weights and incentive values of neuron connections.

Analytical gradient refers to the use of backpropagation in neural network algorithms to calculate the gradient of the objective function with respect to each parameter.

Approximation or approximation means that one thing is similar to another thing, but not exactly the same.

Underfitting refers to the situation where the model does not fit the training data well. It is usually used to evaluate the learning and generalization ability of the model.

The definition of a classifier is to construct a classification model based on existing data. The model can map the data records in the database to one of the given categories, so as to be applied to data prediction. The construction and implementation of the classifier generally go through the following steps: Select samples (including positive samples and negative samples […]

Weight is a relative concept, referring to a certain indicator. The weight of an indicator refers to the relative importance of the indicator in the overall evaluation.

Undersampling is a method to alleviate class imbalance by discarding some samples, that is, appropriately undersampling the categories with a large number of samples (majority classes) in the training set.

Soft margin is a method used to deal with linear inseparable problems and reduce the impact of noise. Soft margin is a method that allows some errors to exist during classification.

The radial basis function (RBF) is a scalar function that is symmetric along the radial direction. It is usually defined as the distance from any point X in space to a certain center X._c It can be written as K ( || X – X _c || ), its effect is often local, that is, when X is far away from X_c The function value is very small.

Quantum computing is a new type of computing based on quantum effects. The basic principle is to use quantum bits as information encoding and storage units, and to complete computing tasks through the controlled evolution of a large number of quantum bits. Comparison between quantum computing and traditional computing (1) Information expression In traditional computing, the unit of computer operation is a ratio of 0 or 1 […]

A quantum computer is a device that uses quantum logic to perform general computations. It is a specific implementation of quantum computing.

Quantum neural network (QNN) is a network composed of several quantum neurons according to a certain topological structure.

Robustness refers to the ability of a computer system to handle errors during execution and the ability of an algorithm to continue to operate normally when encountering anomalies such as input and calculation.

Supervised learning is a machine learning method in which the output is related to the input. A pattern can be learned or established from the training data, and new instances can be inferred based on this pattern.

Structural risk is a compromise between empirical risk and expected risk. A regularization term (penalty term) is added after the empirical risk function to obtain structural risk.

Structural risk minimization (SRM) is an inductive principle in machine learning. It is often used as a strategy to prevent overfitting.

The squeeze function is a function that squeezes a larger range of input into a smaller range. It is often used as an activation function.

Weighted voting is a voting method that takes weights into account.

Neighbor Component Analysis (NCA) is a distance measurement learning method associated with KNN (K Nearest Neighbors), which belongs to supervised learning methods. It was first proposed by Goldberger et al. in 2004.

The intra-class scatter matrix represents the scatter of each sample point around the mean.

Comprehensibility refers to how easy something is to understand, mainly whether it is easy for readers to understand.

Polarity detection is the process of classifying the sentiment polarity of text in natural language.

The activation function is a dynamic principle that is often used in neural network models. It defines how a neuron changes its activation value based on the activity of other neurons. The general activation function depends on the weights in the network, which can introduce nonlinear factors and is often used to solve problems that cannot be solved with linear equations.

A parse tree, also called a concrete syntax tree, is a representation of the results of a syntax analysis, which represents the grammatical structure of a language in a tree shape.

Structure is a method of displaying a neural network topology diagram, which is often used in the field of neural networks. In a neural network, variables can be the weights and incentive values of neuron connections.

Analytical gradient refers to the use of backpropagation in neural network algorithms to calculate the gradient of the objective function with respect to each parameter.

Approximation or approximation means that one thing is similar to another thing, but not exactly the same.